Lane tracking method and lane tracking system for an autonomous vehicle

ABSTRACT

A lane tracking method is proposed for use by an autonomous vehicle running on a lane. A future location of the autonomous vehicle that corresponds to a future time point is estimated based on a current location and a measurement result of an inertial measurement unit of the autonomous vehicle. The future location of the autonomous vehicle, and a reference lane line data and a reference past location that correspond to a reference past time point are used to estimate a future lane line data that corresponds to the future time point.

FIELD

The disclosure relates to control of an autonomous vehicle, and moreparticularly to a lane tracking method and a lane tracking system.

BACKGROUND

According to the levels of driving automation that are formulated by SAEInternational, a higher level of driving automation requires higherrobustness and higher reliability for an active control system of anautonomous vehicle.

Limited by the time required for processing images captured by a cameraof a lane tracking system, an update frequency for lane detectioninformation is low (e.g., 10-20 Hz), and the lane detection informationis hardly truly “real-time” from the perspective of vehicle motioncontrol. For example, the lane detection information, such as laneline/marking information, is usually 100 ms to 200 ms old when providedto a vehicle control system. If a frequency of the vehicle controlsystem controlling operation of the vehicle is limited by, for example,the lower update frequency of the lane detection information, theresolution and the precision of control instructions issued by thevehicle control system may be reduced, and the lane tracking performancemay be adversely affected, which is especially evident in road sectionswith high curvature or when the vehicle is traveling with a largerlateral speed.

In addition, when lane lines/markings have deteriorated, are unclear oreven absent, or present abnormal color contrast due to variations inlight conditions, conventional lane tracking methods may not providecorrect lane line information as normal.

SUMMARY

Therefore, an object of the disclosure is to provide a lane trackingmethod that can alleviate at least one of the drawbacks of the priorart.

According to the disclosure, the lane tracking method for use by anautonomous vehicle is proposed to be implemented by a processing unit.The processing unit stores, into a storage unit, a reference lane linedata piece, a plurality of past location data pieces, and a currentlocation data piece. The reference lane line data piece is generatedbased on an image of a lane on which the autonomous vehicle is located,the image of the lane being captured at a reference past time point by alane detection module mounted to the autonomous vehicle. The pastlocation data pieces include a reference past location data piece thatcorresponds to a vehicle location, which refers to a location of areference point of the autonomous vehicle relative to the lane, at thereference past time point. The current location data piece correspondsto the vehicle location at a current time point. Each of the pastlocation data piece(s) other than the reference past location data piececorresponds to the vehicle location at a respective one of one or morepast time points that are between the reference past time point and thecurrent time point and that are equidistantly separated by a unit timelength. Each of the past location data pieces and the current locationdata piece includes a longitudinal location value, a lateral locationvalue, and an azimuth.

The lane tracking method includes: a) calculating an estimated yaw rateand an estimated lateral acceleration that correspond to the currenttime point based on an angular speed and an acceleration of theautonomous vehicle which are measured by an inertial measurement unit ofthe autonomous vehicle at the current time point, and calculating areference yaw rate and a reference lateral acceleration that correspondto the current time point based on vehicle motion information that isrelated to a steering wheel and wheels of the autonomous vehicle andthat is sensed at the current time point by a motion sensing unitmounted to the autonomous vehicle; b) upon determining that a similaritybetween the estimated yaw rate and the reference yaw rate is at least afirst predetermined confidence level and that a similarity between theestimated lateral acceleration and the reference lateral acceleration isat least a second predetermined confidence level, estimating alongitudinal displacement, a lateral displacement and an azimuthvariation that correspond to a time that is the unit time length laterthan the current time point, based on the estimated yaw rate and theestimated lateral acceleration; c) calculating a future location datapiece that corresponds to the vehicle location at a future time pointbased on the current location data piece, the longitudinal displacement,the lateral displacement and the azimuth variation, and storing thefuture location data piece in the storage unit, the future location datapiece includes a longitudinal location value, a lateral location valueand an azimuth; d) calculating a total longitudinal displacement, atotal lateral displacement and a total azimuth variation of theautonomous vehicle from the reference past time point to the future timepoint based on the reference past location data piece and the futurelocation data piece; e) calculating a future lane line data piece basedon the reference lane line data piece, the total longitudinaldisplacement, the total lateral displacement and the total azimuthvariation, and storing the future lane line data piece in the storageunit; and f) transmitting the future lane line data piece to a controlsystem for the control system to control lateral motion of theautonomous vehicle based on the future lane line data piece.

Another object of the disclosure is to provide a lane tracking systemthat can alleviate at least one of the drawbacks of the prior art.

According to the disclosure, the lane tracking system for use by anautonomous vehicle includes a lane detection module, an inertialmeasurement unit, a motion sensing unit, a storage unit, and aprocessing unit. The lane detection module is mounted to the autonomousvehicle, and is configured to continuously capture, at a detectingfrequency, images of a lane on which of the autonomous vehicle islocated, and to generate a lane line data piece for each image capturedthereby. The inertial measurement unit is mounted to the autonomousvehicle, and is configured to sense inertia of the autonomous vehicleand generate data of an angular speed and an acceleration of theautonomous vehicle. The motion sensing unit is mounted to the autonomousvehicle, and is configured to sense motion of the autonomous vehicle andmotions of a steering wheel and wheels of the autonomous vehicle, and togenerate vehicle motion information. The storage unit stores a referencelane line data piece, a plurality of past location data pieces, and acurrent location data piece. The reference lane line data piece isgenerated based on an image of the lane captured at a reference pasttime point by the lane detection module. The past location data piecesinclude a reference past location data piece that corresponds to avehicle location, which refers to a location of a reference point of theautonomous vehicle relative to the lane, at the reference past timepoint. The current location data piece corresponds to the vehiclelocation at a current time point. Each of the past location datapiece(s) other than the reference past location data piece correspondsto the vehicle location at a respective one of one or more past timepoints that are between the reference past time point and the currenttime point and that are equidistantly separated by a unit time length.Each of the past location data pieces and the current location datapiece includes a longitudinal location value, a lateral location value,and an azimuth. The processing unit is electrically coupled to the lanedetection module, the inertial measurement unit, the motion sensing unitand the storage unit, and is configured to (i) calculate an estimatedyaw rate and an estimated lateral acceleration that correspond to thecurrent time point based on the angular speed and the acceleration ofthe autonomous vehicle which are measured by the inertial measurementunit at the current time point, (ii) calculate a reference yaw rate anda reference lateral acceleration that correspond to the current timepoint based on the vehicle motion information that is related to thesteering wheel and the wheels of the autonomous vehicle and that issensed by the motion sensing unit at the current time point, (iii)estimate, upon determining that a similarity between the estimated yawrate and the reference yaw rate is at least a first predeterminedconfidence level and that a similarity between the estimated lateralacceleration and the reference lateral acceleration is at least a secondpredetermined confidence level, a longitudinal displacement, a lateraldisplacement and an azimuth variation that correspond to a time that isthe unit time length later than the current time point, based on theestimated yaw rate and the estimated lateral acceleration, (iv)calculate a future location data piece that corresponds to the vehiclelocation at a future time point based on the current location datapiece, the longitudinal displacement, the lateral displacement and theazimuth variation, (v) store the future location data piece in thestorage unit, wherein the future location data piece includes alongitudinal location value, a lateral location value and an azimuth,(vi) calculate a total longitudinal displacement, a total lateraldisplacement and a total azimuth variation of the autonomous vehiclefrom the reference past time point to the future time point based on thereference past location data piece and the future location data piece,(vii) calculate a future lane line data piece based on the referencelane line data piece, the total longitudinal displacement, the totallateral displacement and the total azimuth variation, (viii) store thefuture lane line data piece in the storage unit, and (ix) transmit thefuture lane line data piece to a control system for the control systemto control lateral motion of the autonomous vehicle based on the futurelane line data piece.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features and advantages of the disclosure will become apparent inthe following detailed description of the embodiment (s) with referenceto the accompanying drawings, of which:

FIG. 1 is a schematic diagram showing a vehicle running on a lane;

FIG. 2 is a block diagram illustrating an embodiment of the lanetracking system according to this disclosure;

FIG. 3 is a table illustrating data stored in a storage unit of theembodiment of the lane tracking system;

FIG. 4 is a flow chart illustrating steps of an embodiment of the lanetracking method according to this disclosure;

FIG. 5 is a table illustrating data stored in a storage unit of theembodiment after step 406 of the embodiment of the lane tracking method;

FIG. 6 is a table illustrating data stored in a storage unit of theembodiment after step 409 of the embodiment of the lane tracking method;

FIG. 7 is a table illustrating data stored in a storage unit of theembodiment after step 413 of the embodiment of the lane tracking method;and

FIG. 8 is a schematic diagram illustrating an estimated future lane linedata piece that corresponds to a future time point.

DETAILED DESCRIPTION

Before the disclosure is described in greater detail, it should be notedthat where considered appropriate, reference numerals or terminalportions of reference numerals have been repeated among the figures toindicate corresponding or analogous elements, which may optionally havesimilar characteristics.

Referring to FIGS. 1 and 2, the embodiment of the lane tracking system100 according to this disclosure is for use by an autonomous vehicle 300(i.e., a vehicle capable of driving automation) when the autonomousvehicle 300 is running on a lane 200. In this embodiment, two oppositesides of the lane 200 are marked with lane lines in a form of dashedlines. The lane lines generally refer to lane markings, and in otherembodiments, an individual lane line may be formed as a solid line ordouble solid lines, double broken lines, a solid line beside a brokenline, etc., and this disclosure is not limited in this respect. The lanetracking system 100 includes a lane detection module 1, an inertialmeasurement unit 2, a motion sensing unit 3, a storage unit 4 and aprocessing unit 5.

The lane detection module 1 is mounted to the autonomous vehicle 300,and includes, for example, a CCD (charge-coupled device) image sensor11, and an image processor 12 electrically coupled to the CCD imagesensor 11. The CCD image sensor 11 is configured to continuously captureimages of the lane 200 at a detecting frequency (e.g., 10 Hz), and theimage processor 12 employs conventional image processing algorithms togenerate, for each of the images captured by the CCD image sensor 11, alane line data piece based on the image. An update frequency for thelane line data piece is the same as the detecting frequency (i.e., 10 Hzin this embodiment). Due to time required for image processing, the laneline data piece may be generated and outputted by the image processor 12100 ms after the corresponding image is captured by the CCD image sensor11. In this embodiment, the lane line data piece includes, for example,a left lane line equation (equation (1)) and a right lane line equation(equation (2)) in the forms of:

y _(L) =f _(L)(x)=A _(L) x ³ +B _(L) x ² +C _(L) x+D _(L)  (1)

y _(R) =f _(R)(x)=A _(R) x ³ +B _(R) x ² +C _(R) x+D _(R),   (2)

where y_(L) represents a lateral location of a left lane line at alongitudinal location (i.e., a location in a direction where theautonomous vehicle 300 is heading) of x, which is de fined with respectto a vehicle location, where the vehicle location refers to a locationof a reference point 301 of the autonomous vehicle 300 (see FIG. 8, thereference point 301 may be a center of gravity of the autonomous vehicle300 ) relative to the lane 200, and y_(R) represents a lateral locationof a right lane line at the longitudinal location of x.

The inertial measurement unit 2 is mounted to the autonomous vehicle300, and includes, for example, a triaxial gyroscope 21 and a triaxialaccelerometer 22, which are used to measure an angular speed and anacceleration of the autonomous vehicle 300 in the three-dimensionalspace, and generate inertial measurement results that indicate theangular speed and the acceleration thus measured.

The motion sensing unit 3 is mounted to the autonomous vehicle 300, andis configured to sense motion of the autonomous vehicle 300 and motionsof a steering wheel and wheels of the autonomous vehicle 300, and togenerate vehicle motion information accordingly. In this embodiment, themotion sensing unit 3 includes a steering angle sensor 31 to sense asteering angle of the steering wheel of the autonomous vehicle 300, avehicle speed sensor 32 to sense a longitudinal velocity (vehicle speed)of the autonomous vehicle 300, and a wheel speed sensor set 33 to senserotational speeds (wheel speeds) of the wheels of the autonomous vehicle300, but this disclosure is not limited in this respect. In thisembodiment, the vehicle motion information includes the steering angle,the vehicle speed, the wheel speed of a rear right wheel of theautonomous vehicle 300, and the wheel speed of a rear left wheel of theautonomous vehicle 300.

Ideally, the inertial measurement unit 2 and the motion sensing unit 3are designed to have the same output refresh rate, which is assumed tobe ten times the output refresh rate of the lane detection module 1 inthis embodiment. For example, the outputs of the inertial measurementunit 2 and the motion sensing unit 3 are updated every 10 ms, whichmeans that the update frequency (refresh rate) is 100 Hz, and the timelength 10 ms is referred to as the unit time length in this example.

Referring to FIG. 3, the storage unit 4, which may be a non-volatilememory device, such as a hard disk drive, a solid state drive, a flashmemory device, etc., stores a reference lane line data piece, anestimated lane line data piece, a plurality of past location datapieces, and a current location data piece. The reference lane line datapiece is generated based on an image of the lane 200 that was capturedat a reference past time point (t_(0−N)) by the lane detection module 1.It is noted that the generation of the reference lane line data pieceoccurs for example, 100 ms after the reference past time point (t_(0−N))in view of the time required for image processing. The estimated laneline data piece was generated previously and corresponds to a currenttime point (t₀). The past location data pieces include a reference pastlocation data piece that corresponds to the vehicle location at thereference past time point (t_(0−N)). The current location data piececorresponds to the vehicle location at the current time point (t₀). Eachof the past location data piece (s) other than the reference pastlocation data piece corresponds to the vehicle location at a respectiveone of one or more past time points (t⁰⁻¹, . . . , t₀−(N−1)) that arebetween the reference past time point (t_(0−N)) and the current timepoint (t₀) and that are equidistantly separated by the unit time length.For example, the past time point (t⁰⁻¹) is prior to the current timepoint (t₀) by one unit time length, the past time point (t⁰⁻²) is priorto the past time point (t⁰⁻¹) by one unit time length, and so on.Accordingly, the reference past time point (t_(0−N)) is prior to thecurrent time point (t₀) by a number (N) of the unit time lengths, whereN is a positive integer. In this embodiment, each of the reference laneline data piece and the estimated lane line data piece includes a leftlane line equation and a right lane line equation, and each of the pastlocation data pieces and the current location data piece includes alongitudinal location value, a lateral location value, and an azimuth.

The processing unit 5 is electrically coupled to the lane detectionmodule 1 for receiving the lane line data piece, is electrically coupledto the inertial measurement unit 2 for receiving the inertialmeasurement results, is electrically coupled to the motion sensing unit3 for receiving the vehicle motion information, and is electricallycoupled to the storage unit 4 for storing data received thereby in thestorage unit 4 and for reading data stored in the storage unit 4. Theprocessing unit 5 is a hardware device which may also be called, forexample, a central processing unit (CPU), a controller, a processor, orthe like. It is noted that, in this embodiment, all the data stored inthe storage unit 4 are obtained by the processing unit 5 repeatedlyperforming an embodiment of the lane tracking method according to thisdisclosure, which will be described hereinafter, and are stored in thestorage unit 4 by the processing unit 5.

Referring to FIGS. 2 and 4, and also FIGS. 3 and 5-7, the embodiment ofthe lane tracking method according to this disclosure will be describedto show how to estimate a future lane line data piece that correspondsto a future time point (t₀₊₁) for the autonomous vehicle 300.

In step 401, the processing unit 5 acquires an estimated yaw rate and anestimated lateral acceleration that correspond to the current time point(t₀) based on the inertial measurement results (i.e., the angular speedand the acceleration of the autonomous vehicle 300) measured andgenerated/outputted by the inertial measurement unit 2 at the currenttime point (t₀). In this embodiment, the processing 5 uses a Kalmanfilter to filter out noises of the measured angular speed andacceleration, and then uses the Kalman filter to perform estimationbased on the angular speed and acceleration of which the noises havebeen filtered out, so as to obtain the estimated yaw rate and theestimated lateral acceleration. Since the Kalman filter is known in theart, details thereof are omitted herein for the sake of brevity.

In step 402, the processing unit 5 acquires a reference yaw rate and areference lateral acceleration that correspond to the current time point(t₀) based on the vehicle motion information that is sensed andgenerated/outputted by the motion sensing unit 3 at the current timepoint (t₀). In this embodiment, the processing unit 5 calculates thereference yaw rate ({circumflex over (γ)}) and the reference lateralacceleration (â_(y)) based on the steering angle (δ_(sw)) and thevehicle speed (V_(x)) of the vehicle motion information, and a steeringratio (N), an understeering coefficient (K_(us)) and a wheelbase (L) ofthe autonomous vehicle 300 according to equations (3) and (4) thatfollow:

{circumflex over (γ)}=δ_(f) ·V _(x)/(L+K _(ux) ·V _(x) ²)  (3)

â_(y)={circumflex over (γ)}·V _(x)=δ_(f) ·V _(x) ²/(L+K _(us) ·V _(x)²),  (4)

where δ_(f) is a turn of front wheel (in degrees) and δ_(f)=δ_(sw)/N. Inanother embodiment, the processing unit calculates the reference yawrate ({circumflex over (γ)}) and the reference lateral acceleration(â_(y)) based on the vehicle speed (V_(x)), the wheel speed of the rearright wheel (ν_(rr)) and the wheel speed of the rear left wheel (ν_(rl))of the vehicle motion information, and a rear track width (S_(r)) of theautonomous vehicle 300 according to equations (5) and (6) that follow:

{circumflex over (γ)}=(V _(rr) −V _(rl))/S _(r)  (5)

â_(y)={circumflex over (γ)}·V _(x) =V _(x)(V _(rr) −V _(rl))/S _(r)  (6)

In yet another embodiment, the processing unit 5 calculates thereference yaw rate ({circumflex over (γ)}) and the reference lateralacceleration (â_(y)) based on the turn of front wheel (δ_(f)) and thewheel speed of a front right wheel (v_(fr)) and the wheel speed of afront left wheel (v_(fl)) of the vehicle motion information according toequations (7) and (8) that follow:)

{circumflex over (γ)}=(V _(fr) −V _(fl))/S _(f) cos(δ_(f))  (7)

â_(y)={circumflex over (γ)}·V _(x) =V _(x)·(V _(fr) −V _(fl))/S _(f)cos(δ_(f))  (8)

After steps 401 and 402, the processing unit 5 may use, for example, atwo sample T-test to determine whether a similarity between theestimated yaw rate and the reference yaw rate is at least a firstpredetermined confidence level and whether a similarity between theestimated lateral acceleration and the reference lateral acceleration isat least a second predetermined confidence level (step 403). In thisembodiment, both of the first predetermined confidence level and thesecond predetermined confidence level are 95%, but this disclosure isnot limited to such. The flow goes to step 405 when the determination isaffirmative, and goes to step 404 when otherwise, which means that theinertial measurement result is not reliable. In step 404, the processingunit 5 outputs a warning signal that indicates abnormality of theinertial measurement unit 2 to an external control system (not shown),so that the external control system can follow up on this matter.

In step 405, the processing unit 5 estimates a longitudinal displacement(Δs_(x)), a lateral displacement (Δs_(y)) and an azimuth variation (Δϕ)that correspond to a time the unit time length (e.g., 10 ms) later thanthe current time point (t₀) (i.e., the future time point (t₀₊₁)), basedon the estimated yaw rate and the estimated lateral acceleration. Then,the processing unit 5 calculates a future location data piece thatcorresponds to the vehicle location at the future time point (t₀₊₁)based on the current location data piece, the longitudinal displacement(Δs_(x)), the lateral displacement (Δs_(y)) and the azimuth variation(Δϕ), and stores the future location data piece in the storage unit 4(step 406 ). The future location data piece includes a longitudinallocation value, a lateral location value and an azimuth, as shown inFIG. 5. After step 406, the flow goes to step 412.

It is noted that, during steps 401 to 406, the processing unit 5 maydetermine whether a new lane line data piece is received at the currenttime point (t₀) from the lane detection module 1 (step 407). If a newlane line data piece received when step 406 is finished, the data storedin the storage unit 4 may be updated prior to performing step 412, sothat step 412 can be performed using the updated data. Upon determiningthat a new lane line data piece, which is generated at the current timepoint (t₀) by the image processor 12 based on an image of the lane 200captured by the CCD image sensor 11 at one of the past time points(e.g., t⁰⁻¹⁰), is received at the current time point (t₀) in step 407,the processing unit 5 determines whether the new lane line data piece isreliable (step 408) based on the estimated lane line data piece and apredetermined reference condition that relates to an image sensingspecification (e.g., image sensing resolution) of the CCD image sensor11, so as to check if the lane detection module 1 works normally.

In this embodiment, the image sensing specification defines a farthestlongitudinal location and a nearest longitudinal location between whichprecision of image sensing by the CCD image sensor 11 is reliable. Thepredetermined reference condition includes a first difference thresholdrelated to a width of a detected lane at the nearest longitudinallocation, a second difference threshold related to a width of thedetected lane at the farthest longitudinal location, a first deviationthreshold related to a central line of the detected lane at the nearestlongitudinal location, and a second deviation threshold related to thecentral line of the detected lane at the farthest longitudinal location.In one example, the farthest longitudinal location is 25 meters from thevehicle location, a nearest longitudinal location is 15 meters from thevehicle location, the first difference threshold and the seconddifference threshold are both 0.5 meters, and the first deviationthreshold and the second deviation threshold are both 0.1 meters, butthis disclosure is not limited in this respect.

In step 408, the processing unit 5 calculates, based on the estimatedlane line data piece, a first width value representing a width of thelane 200 at the nearest longitudinal location, a first location valuerepresenting a lateral location of a central line of the lane 200 at thenearest longitudinal location, a second width value representing a widthof the lane 200 at the farthest longitudinal location, and a secondlocation value representing a lateral location of the central line ofthe lane 200 at the farthest longitudinal location. The processing unit5 also calculates, based on the new lane line data piece, a third widthvalue representing the width of the lane 200 at the nearest longitudinallocation, a third location value representing the lateral location ofthe central line of the lane 200 at the nearest longitudinal location, afourth width value representing the width of the lane 200 at thefarthest longitudinal location, and a fourth location value representingthe lateral location of the central line of the lane 200 at the farthestlongitudinal location. Then, the processing unit 5 calculates a firstdifference between the first width value and the third width value, asecond difference between the second width value and the fourth widthvalue, a third difference between the first location value and the thirdlocation value, and a fourth difference between the second locationvalue and the fourth location value. In this embodiment, the processingunit 5 determines whether the new lane line data piece is reliable bydetermining whether the first difference is not greater than the firstdifference threshold, whether the second difference is not greater thanthe second difference threshold, whether the third difference is notgreater than the first deviation threshold, and whether the fourthdifference is not greater than the second deviation threshold. When allof the abovementioned conditions are satisfied, i.e., the firstdifference is not greater than the first difference threshold, thesecond difference is not greater than the second difference threshold,the third difference is not greater than the first deviation threshold,and the fourth difference is not greater than the second deviationthreshold, the processing unit 5 determines that the new lane line datapiece is reliable, which also means that the lane detection module 1works normally, and the flow goes to step 409. Otherwise, the flow goesto step 410.

For example, it is assumed that the estimated lane line data pieceincludes a left lane line equation (equation (9)) and a right lane lineequation (equation (10)) of:

y′ _(L1) =f′ _(L1)(x)=A′ _(L1) x ³ +B′ _(L1) x ² +C′ _(L1) x+D′_(L1)  (9)

y′ _(R1) =f′ _(R1)(x)=A′ _(R1) x ³ +B′ _(R1) x ³ +C′ _(R1) x+D′_(R1),  (10)

and the new lane line data piece includes a left lane line equation(equation (11)) and a right lane line equation (equation (12)) of:

y′ _(L) =f′ _(L)(x)=′ _(L) x ³ +B′ _(L) x ² +C′ _(L) x+D′ _(L)  (11)

y′ _(R) =f′ _(R)(x)=′ _(R) x ³ +B′ _(R) x ² +C′ _(R) x+D′ _(R)  (12)

Following the previously mentioned example where the farthestlongitudinal location is 25 meters, the nearest longitudinal location is15 meters, the first difference threshold and the second differencethreshold are both 0.5 meters, and the first deviation threshold and thesecond deviation threshold are both 0.1 meters, the first width value(W1), the third width value (W3), the first location value (Y1) and thethird location value (Y3) can be acquired by applying x=15 to equations(9) to (12), where W1=f′_(L1)(15)−f′_(R1)(15), W3=f′_(L)(15)−f′_(R)(15),Y1=(f′_(L1)(15)+f′_(R1)(15)/2, and Y3=(f′_(L)(15)+f′_(R)(15)/2.Similarly, the second width value (W2), the fourth width value (W4), thesecond location value (Y2) and the fourth location value (Y4) can beacquired by applying x=25 (meters) to equations (9) to (12), whereW2=f′_(L1)(25)−f′_(R1)(25), W4=f′_(L)(25)−f′_(R)(25) ,Y2=(f′_(L1)(25)+f′_(R1)(25))/2, and Y4=(f′_(L)(25)+f′_(R)(25)/2. Then,the first difference (D1), the second difference (D2), the thirddifference (D3) and the fourth difference (D4) can be acquired, whereD1=|W1−W3|, D2=|W2−W4|, D3=|Y1−Y3|, and D4=|Y2−Y4|. It is noted that theabovementioned values are represented in meters. If D1≤0.5, D2≤0.5,D3≤0.1 and D4≤0.1, the processing unit 5 determines that the new laneline data piece is reliable. If any one of the above inequalities is notsatisfied, the processing unit 5 determines that the new lane line datapiece is not reliable, which means that the lane detection module 1 maybe temporarily invalid because the lane lines have deteriorated, areunclear or absent, or have abnormal color contrast due to variations inlight condition.

In step 410 (i.e., the new lane line data piece is determined to be notreliable), the processing unit 5 checks whether an accumulated number ofthe new lane line data piece being determined to be not reliable(hereinafter also referred to as “accumulated non-reliable number”)exceeds a threshold number (e.g., 7). When affirmative, the flow goes tostep 411, where the processing unit 5 outputs a warning signal thatindicates abnormality of the lane detection module 1 to the externalcontrol system, so that the external control system can follow up onthis matter. Otherwise, the flow goes to step 412.

In step 409 (i.e., the new lane line data piece is determined to bereliable), the processing unit 5 updates the reference lane line datapiece to the new lane line data piece (i.e., takes the new lane linedata piece as the reference lane line data piece), and updates thereference past location data piece to one of the past location datapieces that corresponds to one of the past time points at which theimage of the lane 200 corresponding to the new lane line data piece wascaptured (i.e., takes the one of the past location data pieces as thereference past location data piece). When the new lane line data pieceis generated based on the image of the lane 200 captured at the pasttime point (t⁰⁻¹⁰), the past location data piece that corresponds to thepast time point (t⁰⁻¹⁰) is stored as the reference past location datapiece (also, the reference past time point is updated from t_(0−N) tot⁰⁻¹⁰) and the past location data pieces that correspond to the pasttime points prior to the past time point (t⁰⁻¹⁰) may be removed, asshown in FIG. 6.

In step 412, the processing unit 5 calculates a total longitudinaldisplacement (S_(x)), a total lateral displacement (S_(y)) and a totalazimuth variation (Ψ) of the autonomous vehicle 300 from the referencepast time point (t_(0−N) or t⁰⁻¹⁰) to the future time point (t₀₊₁) basedon the reference past location data piece and the future location datapiece. Specifically, when it is determined in step 407 that no new laneline data piece is received or it is determined in step 408 that thereceived new lane line data piece is not reliable and the accumulatednon-reliable number does not exceed the threshold number, the processingunit 5 uses the non-updated reference past location data piece (e.g.,corresponding to the (reference) past time point t_(0−N), as shown inFIG. 5) and the future location data piece for calculation in step 412.On the other hand, when it is determined in step 407 that a new laneline data piece is received and it is determined in step 408 that thenew lane line data piece is reliable, the processing unit 5 uses thereference past location data piece updated in step 409 (e.g.,corresponding to the (reference) past time point t⁰⁻¹⁰, as shown in FIG.6) and the future location data piece for calculation in step 412.

In step 413, the processing unit calculates/estimates a future lane linedata piece based on the reference lane line data piece, the totallongitudinal displacement (S_(x)), the total lateral displacement(S_(y)) and the total azimuth variation (Ψ). The future lane line datapiece calculated in step 413 would serve as an estimated lane line datapiece that corresponds to the future time point (t₀₊₁). Then, theprocessing unit 5 stores the future lane line data piece in the storageunit 4 by, for example, overwriting the estimated lane line data piecethat is originally stored in the storage unit 4 and that corresponds tothe current time point (t₀), as shown in FIG. 7, but this disclosure isnot limited in this respect.

For instance, referring to FIG. 8, the left lane line equation (equation(13)) and the right lane line equation (equation (14)) of the estimatedlane line data piece are assumed to be:

y _(L1) =f _(L1)(x)=A _(L1) x ³ +B _(L1) x ² +C _(L1) x+D′ _(L1)  (9)

y′ _(R1) =f′ _(R1)(x)=A′ _(R1) x ³ +B′ _(R1) x ³ +C′ _(R1) x+D′_(R1)  (10)

, and the vehicle location (the location of the reference point 301 ofthe autonomous vehicle 300 relative to the lane 200 ) at the future timepoint (t₀₊₁) with respect to the vehicle location at the reference timepoint (t_(0−N) or t⁰⁻¹⁰) can be represented by:

$\begin{matrix}{\begin{bmatrix}x^{\prime} \\y^{\prime}\end{bmatrix} = {\begin{bmatrix}{\cos \mspace{11mu} \psi} & {\sin \mspace{11mu} \psi} \\{{- \sin}\mspace{11mu} \psi} & {\cos \mspace{11mu} \psi}\end{bmatrix}\begin{bmatrix}{x - S_{x}} \\{y - S_{y}}\end{bmatrix}}} & (15)\end{matrix}$

where x′ and y′ cooperatively represent the vehicle location at thefuture time point (t₀₊₁), and x, y cooperatively represent the vehiclelocation at the reference time point (t_(0−N) or t⁰⁻¹⁰). By using anoperation of matrix inverse, it can be derived from equation (15) that:

$\begin{matrix}{{\begin{bmatrix}x \\y\end{bmatrix} = {{\begin{bmatrix}{\cos \mspace{11mu} \psi} & {{- \sin}\mspace{11mu} \psi} \\{\sin \mspace{11mu} \psi} & {\cos \mspace{11mu} \psi}\end{bmatrix}\begin{bmatrix}x^{\prime} \\y^{\prime}\end{bmatrix}} + \begin{bmatrix}S_{x} \\S_{y}\end{bmatrix}}},} & (16)\end{matrix}$

which shows that x is a function of x′ (x=g(x′,y′)) and y is a functionof y′ (y=h(x′,y′)). Based on equations (13), (14), the left lane lineequation (equation (17)) and the right lane line equation (equation(18)) of the future lane line data piece can be acquired according to:

y _(L2) =f _(L1)(g(x′,y′))=A _(L2) x ³ +B _(L2) x ² +C′ _(L2) x+D′_(L2)  (17)

y _(R2) =f _(R1)(g)(x′, y′))=A _(R2) x ³ +B _(R2) x ³ +C _(R2) x+D_(R2)  (18)

Then, the processing unit 5 transmits the future lane line data piece tothe external control system for the external control system to controllateral motion of the autonomous vehicle 300 based on the future laneline data piece. In the previous example where the refresh rate of thelane detection module 1 is 10 Hz and the refresh rates of the inertialmeasurement unit 2 and the motion sensing unit 3 are both 100 Hz, thelane tracking system 100 that implements the embodiment of the lanetracking method according to this disclosure promotes the refresh rateof the lane line data piece to ten times the refresh rate of the lanedetection module 1 (from 10 Hz to 100 Hz).

In summary, the lane tracking system 100 of this disclosure uses thevehicle motion information to check the reliability of the estimated yawrate and the estimated lateral acceleration that are estimated based onthe inertial measurement result (the angular speed and the accelerationof the autonomous vehicle 300) outputted by the inertial measurementunit 2. Then, the estimated future location data piece and the referencelane line data piece are used to estimate the future lane line datapiece at the future time point, thus promoting the refresh rate of thelane line data piece that contributes to lateral trajectory tracking,and effectively compensating for temporary failure of the lane detectionmodule 1 (i.e., the condition of the new lane line data piece beingdetermined to be not reliable). Accordingly, robustness and precisionfor lateral control can be improved.

In the description above, for the purposes of explanation, numerousspecific details have been set forth in order to provide a thoroughunderstanding of the embodiment(s). It will be apparent, however, to oneskilled in the art, that one or more other embodiments maybe practicedwithout some of these specific details. It should also be appreciatedthat reference throughout this specification to “one embodiment,” “anembodiment,” an embodiment with an indication of an ordinal number andso forth means that a particular feature, structure, or characteristicmay be included in the practice of the disclosure. It should be furtherappreciated that in the description, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure and aiding in theunderstanding of various inventive aspects, and that one or morefeatures or specific details from one embodiment may be practicedtogether with one or more features or specific details from anotherembodiment, where appropriate, in the practice of the disclosure.

While the disclosure has been described in connection with what is (are)considered the exemplary embodiment(s), it is understood that thisdisclosure is not limited to the disclosed embodiment(s) but is intendedto cover various arrangements included within the spirit and scope ofthe broadest interpretation so as to encompass all such modificationsand equivalent arrangements.

What is claimed is:
 1. A lane tracking method for use by an autonomousvehicle, being implemented by a processing unit, and comprising: (A)storing, into a storage unit, a reference lane line data piece, aplurality of past location data pieces, and a current location datapiece, wherein: the reference lane line data piece is generated based onan image of a lane on which the autonomous vehicle is located, the imageof the lane being captured at a reference past time point by a lanedetection module mounted to the autonomous vehicle; the past locationdata pieces include a reference past location data piece thatcorresponds to a vehicle location, which refers to a location of areference point of the autonomous vehicle relative to the lane, at thereference past time point; the current location data piece correspondsto the vehicle location at a current time point; each of the pastlocation data piece(s) other than the reference past location data piececorresponds to the vehicle location at a respective one of one or morepast time points that are between the reference past time point and thecurrent time point and that are equidistantly separated by a unit timelength; and each of the past location data pieces and the currentlocation data piece includes a longitudinal location value, a laterallocation value, and an azimuth; (B) acquiring an estimated yaw rate andan estimated lateral acceleration that correspond to the current timepoint based on an angular speed and an acceleration of the autonomousvehicle which are measured by an inertial measurement unit of theautonomous vehicle at the current time point, and acquiring a referenceyaw rate and a reference lateral acceleration that correspond to thecurrent time point based on vehicle motion information that is relatedto a steering wheel and wheels of the autonomous vehicle and that issensed at the current time point by a motion sensing unit mounted to theautonomous vehicle; (C) upon determining that a similarity between theestimated yaw rate and the reference yaw rate is at least a firstpredetermined confidence level and that a similarity between theestimated lateral acceleration and the reference lateral acceleration isat least a second predetermined confidence level, estimating alongitudinal displacement, a lateral displacement and an azimuthvariation that correspond to a time the unit time length later than thecurrent time point, based on the estimated yaw rate and the estimatedlateral acceleration; (D) calculating a future location data piece thatcorresponds to the vehicle location at a future time point based on thecurrent location data piece, the longitudinal displacement, the lateraldisplacement and the azimuth variation, and storing the future locationdata piece in the storage unit, the future location data piece includinga longitudinal location value, a lateral location value and an azimuth;(E) calculating a total longitudinal displacement, a total lateraldisplacement and a total azimuth variation of the autonomous vehiclefrom the reference past time point to the future time point based on thereference past location data piece and the future location data piece;(F) calculating a future lane line data piece based on the referencelane line data piece, the total longitudinal displacement, the totallateral displacement and the total azimuth variation, and storing thefuture lane line data piece in the storage unit; and (G) transmittingthe future lane line data piece to a control system for the controlsystem to control lateral motion of the autonomous vehicle based on thefuture lane line data piece.
 2. The lane tracking method of claim 1,wherein step (A) further includes storing, in the storage unit, anestimated lane line data piece that was estimated previously and thatcorresponds to the current time point, said lane tracking method furthercomprising: (H) upon receipt of a new lane line data piece that isgenerated based on an image of the lane which was captured at one of thepast time points by the lane detection module, determining whether thenew lane line data piece is reliable based on the estimated lane linedata piece and a predetermined reference condition that relates to animage sensing specification of the lane detection module; and (I) upondetermining that the new lane line data piece is reliable, updating thereference lane line data piece and the reference past location datapiece to the new lane line data piece and one of the past location datapieces that corresponds to said one of the past time points,respectively.
 3. The lane tracking method of claim 2, wherein: the imagesensing specification defines a farthest longitudinal location and anearest longitudinal location between which precision of image sensingis reliable; the predetermined reference condition includes a firstdifference threshold related to a width of a detected lane at thenearest longitudinal location, a second difference threshold related tothe width of the detected lane at the farthest longitudinal location, afirst deviation threshold related to a central line of the detected laneat the nearest longitudinal location, and a second deviation thresholdrelated to the central line of the detected lane at the farthestlongitudinal location; wherein step (H) includes: calculating, based onthe estimated lane line data piece, a first width value representing awidth of the lane at the nearest longitudinal location, a first locationvalue representing a lateral location of a central line of the lane atthe nearest longitudinal location, a second width value representing awidth of the lane at the farthest longitudinal location, and a secondlocation value representing a lateral location of the central line ofthe lane at the farthest longitudinal location; calculating, based onthe new lane line data piece, a third width value representing the widthof the lane at the nearest longitudinal location, a third location valuerepresenting the lateral location of the central line of the lane at thenearest longitudinal location, a fourth width value representing thewidth of the lane at the farthest longitudinal location, and a fourthlocation value representing the lateral location of the central line ofthe lane at the farthest longitudinal location; calculating a firstdifference between the first width value and the third width value, asecond difference between the second width value and the fourth widthvalue, a third difference between the first location value and the thirdlocation value, and a fourth difference between the second locationvalue and the fourth location value; and determining that the new laneline data piece is reliable when the first difference is not greaterthan the first difference threshold, the second difference is notgreater than the second difference threshold, the third difference isnot greater than the first deviation threshold, and the fourthdifference is not greater than the second deviation threshold.
 4. Thelane tracking method of claim 1, wherein step (B) includes: using aKalman filter to filter out noises of the angular speed and theacceleration of the autonomous vehicle which are measured by theinertial measurement unit and to obtain the estimated yaw rate and theestimated lateral acceleration based on the angular speed and theacceleration of which the noises have been filtered out.
 5. The lanetracking method of claim 1, wherein: the vehicle motion informationincludes a steering angle and a vehicle speed; and step (B) includes:obtaining the reference yaw rate and the reference lateral accelerationbased on the steering angle, the vehicle speed, and a steering ratio, anundersteering coefficient and a wheelbase of the autonomous vehicle. 6.The lane tracking method of claim 1, wherein: the vehicle motioninformation includes a vehicle speed, a wheel speed of a rear rightwheel, and a wheel speed of a rear left wheel; and step (B) includes:obtaining the reference yaw rate and the reference lateral accelerationbased on the vehicle speed, the wheel speed of the rear right wheel, thewheel speed of the rear left wheel, and a rear track width of theautonomous vehicle.
 7. A lane tracking system for use by an autonomousvehicle, comprising: a lane detection module mounted to the autonomousvehicle, and configured to continuously capture, at a detectingfrequency, images of a lane on which of the autonomous vehicle islocated , and to generate a lane line data piece for each image capturedthereby; an inertial measurement unit mounted to the autonomous vehicle,and configured to sense inertia of the autonomous vehicle and generatedata of an angular speed and an acceleration of the autonomous vehicle;a motion sensing unit mounted to the autonomous vehicle, and configuredto sense motion of the autonomous vehicle and motions of a steeringwheel and wheels of the autonomous vehicle, and to generate vehiclemotion information; a storage unit storing a reference lane line datapiece, a plurality of past location data pieces, and a current locationdata piece, wherein: the reference lane line data piece is generatedbased on an image of the lane captured at a reference past time point bysaid lane detection module; the past location data pieces include areference past location data piece that corresponds to a vehiclelocation, which refers to a location of a reference point of theautonomous vehicle relative to the lane, at the reference past timepoint; the current location data piece corresponds to the vehiclelocation at a current time point; each of the past location datapiece(s) other than the reference past location data piece correspondsto the vehicle location at a respective one of one or more past timepoints that are between the reference past time point and the currenttime point and that are equidistantly separated by a unit time length;and each of the past location data pieces and the current location datapiece includes a longitudinal location value, a lateral location value,and an azimuth; and a processing unit electrically coupled to said lanedetection module, said inertial measurement unit, said motion sensingunit and said storage unit, and configured to calculate an estimated yawrate and an estimated lateral acceleration that correspond to thecurrent time point based on the angular speed and the acceleration ofthe autonomous vehicle which are measured by said inertial measurementunit at the current time point, calculate a reference yaw rate and areference lateral acceleration that correspond to the current time pointbased on the vehicle motion information that is related to the steeringwheel and the wheels of the autonomous vehicle and that is sensed bysaid motion sensing unit at the current time point, estimate, upondetermining that a similarity between the estimated yaw rate and thereference yaw rate is at least a first predetermined confidence leveland that a similarity between the estimated lateral acceleration and thereference lateral acceleration is at least a second predeterminedconfidence level, a longitudinal displacement, a lateral displacementand an azimuth variation that correspond to a time the unit time lengthlater than the current time point, based on the estimated yaw rate andthe estimated lateral acceleration, calculate a future location datapiece that corresponds to the vehicle location at a future time pointbased on the current location data piece, the longitudinal displacement,the lateral displacement and the azimuth variation, store the futurelocation data piece in said storage unit, wherein the future locationdata piece includes a longitudinal location value, a lateral locationvalue and an azimuth, calculate a total longitudinal displacement, atotal lateral displacement and a total azimuth variation of theautonomous vehicle from the reference past time point to the future timepoint based on the reference past location data piece and the futurelocation data piece, calculate a future lane line data piece based onthe reference lane line data piece, the total longitudinal displacement,the total lateral displacement and the total azimuth variation, storethe future lane line data piece in said storage unit, and transmit thefuture lane line data piece to a control system for the control systemto control lateral motion of the autonomous vehicle based on the futurelane line data piece.
 8. The lane tracking system of claim 7, whereinsaid storage unit further stores an estimated lane line data piece thatwas estimated previously and that corresponds to the current time point;wherein said processing unit is further configured to determine, uponreceipt of a new lane line data piece that is generated based on animage of the lane which was captured at one of the past time points bysaid lane detection module, whether the new lane line data piece isreliable based on the estimated lane line data piece and a predeterminedreference condition that relates to an image sensing specification ofsaid lane detection module, and update, upon determining that the newlane line data piece is reliable, the reference lane line data piece andthe reference past location data piece to the new lane line data pieceand one of the past location data pieces that corresponds to said one ofthe past time points, respectively.
 9. The lane tracking system of claim8, wherein: the image sensing specification defines a farthestlongitudinal location and a nearest longitudinal location between whichprecision of image sensing is reliable; the predetermined referencecondition includes a first difference threshold related to a width of adetected lane at the nearest longitudinal location, a second differencethreshold related to the width of the detected lane at the farthestlongitudinal location, a first deviation threshold related to a centralline of the detected lane at the nearest longitudinal location, and asecond deviation threshold related to the central line of the detectedlane at the farthest longitudinal location; said processing unit isfurther configured to calculate, based on the estimated lane line datapiece, a first width value representing a width of the lane at thenearest longitudinal location, a first location value representing alateral location of a central line of the lane at the nearestlongitudinal location, a second width value representing a width of thelane at the farthest longitudinal location, and a second location valuerepresenting a lateral location of the central line of the lane at thefarthest longitudinal location, calculating, based on the new lane linedata piece, a third width value representing the width of the lane atthe nearest longitudinal location, a third location value representingthe lateral location of the central line of the lane at the nearestlongitudinal location, a fourth width value representing the width ofthe lane at the farthest longitudinal location, and a fourth locationvalue representing the lateral location of the central line of the laneat the farthest longitudinal location, calculate a first differencebetween the first width value and the third width value, a seconddifference between the second width value and the fourth width value, athird difference between the first location value and the third locationvalue, and a fourth difference between the second location value and thefourth location value, and determine that the new lane line data pieceis reliable when the first difference is not greater than the firstdifference threshold, the second difference is not greater than thesecond difference threshold, the third difference is not greater thanthe first deviation threshold, and the fourth difference is not greaterthan the second deviation threshold.
 10. The lane tracking system ofclaim 7, wherein said processing unit is configured to use a Kalmanfilter to filter out noises of the angular speed and the acceleration ofthe autonomous vehicle which are measured by the inertial measurementunit and to obtain the estimated yaw rate and the estimated lateralacceleration based on the angular speed and the acceleration of whichthe noises have been filtered out.
 11. The lane tracking system of claim7, wherein: the vehicle motion information includes a steering angle anda vehicle speed; and said processing unit is configured to obtain thereference yaw rate and the reference lateral acceleration based on thesteering angle, the vehicle speed, and a steering ratio, anundersteering coefficient and a wheelbase of the autonomous vehicle. 12.The lane tracking system of claim 7, wherein: the vehicle motioninformation includes a vehicle speed, a wheel speed of a rear rightwheel, and a wheel speed of a rear left wheel; and said processing unitis configured to obtain the reference yaw rate and the reference lateralacceleration based on the vehicle speed, the wheel speed of the rearright wheel, the wheel speed of the rear left wheel, and a rear trackwidth of the autonomous vehicle.